Overview

Dataset statistics

Number of variables19
Number of observations149956
Missing cells1929140
Missing cells (%)67.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 MiB
Average record size in memory160.0 B

Variable types

Numeric12
Categorical4
Text2
DateTime1

Alerts

footnote has constant value ""Constant
data_type is highly imbalanced (50.1%)Imbalance
derivation_id is highly imbalanced (89.3%)Imbalance
unit_name is highly imbalanced (51.9%)Imbalance
data_type has 114578 (76.4%) missing valuesMissing
description has 114586 (76.4%) missing valuesMissing
food_category_id has 114600 (76.4%) missing valuesMissing
publication_date has 114578 (76.4%) missing valuesMissing
id has 35378 (23.6%) missing valuesMissing
nutrient_id has 35851 (23.9%) missing valuesMissing
amount has 35851 (23.9%) missing valuesMissing
data_points has 38324 (25.6%) missing valuesMissing
derivation_id has 35860 (23.9%) missing valuesMissing
min has 139150 (92.8%) missing valuesMissing
max has 139150 (92.8%) missing valuesMissing
median has 138548 (92.4%) missing valuesMissing
footnote has 149953 (> 99.9%) missing valuesMissing
min_year_acqured has 124305 (82.9%) missing valuesMissing
name has 149483 (99.7%) missing valuesMissing
unit_name has 149483 (99.7%) missing valuesMissing
nutrient_nbr has 149495 (99.7%) missing valuesMissing
rank has 149494 (99.7%) missing valuesMissing
amount is highly skewed (γ1 = 45.27538282)Skewed
min is highly skewed (γ1 = 57.3891599)Skewed
max is highly skewed (γ1 = 44.33747669)Skewed
median is highly skewed (γ1 = 52.0960738)Skewed
amount has 25966 (17.3%) zerosZeros
min has 3430 (2.3%) zerosZeros
max has 2422 (1.6%) zerosZeros
median has 2967 (2.0%) zerosZeros

Reproduction

Analysis started2023-06-21 23:05:45.346755
Analysis finished2023-06-21 23:06:02.552043
Duration17.21 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

fdc_id
Real number (ℝ)

Distinct35378
Distinct (%)23.7%
Missing473
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean753370.81
Minimum319874
Maximum2007412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:02.714641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum319874
5-th percentile322258.1
Q1328451
median335473
Q3790379
95-th percentile2003599
Maximum2007412
Range1687538
Interquartile range (IQR)461928

Descriptive statistics

Standard deviation612415.95
Coefficient of variation (CV)0.81290108
Kurtosis-0.2804282
Mean753370.81
Median Absolute Deviation (MAD)12582
Skewness1.1659583
Sum1.1261613 × 1011
Variance3.750533 × 1011
MonotonicityNot monotonic
2023-06-21T20:06:02.812000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999630 160
 
0.1%
322228 158
 
0.1%
746782 158
 
0.1%
1750337 158
 
0.1%
322559 158
 
0.1%
746778 158
 
0.1%
321359 158
 
0.1%
746776 158
 
0.1%
322892 158
 
0.1%
746772 158
 
0.1%
Other values (35368) 147901
98.6%
(Missing) 473
 
0.3%
ValueCountFrequency (%)
319874 1
 
< 0.1%
319875 1
 
< 0.1%
319876 1
 
< 0.1%
319877 5
< 0.1%
319878 10
< 0.1%
319879 1
 
< 0.1%
319880 1
 
< 0.1%
319881 1
 
< 0.1%
319882 5
< 0.1%
319883 2
 
< 0.1%
ValueCountFrequency (%)
2007412 2
< 0.1%
2007411 2
< 0.1%
2007410 2
< 0.1%
2007409 2
< 0.1%
2007408 2
< 0.1%
2007407 2
< 0.1%
2007406 2
< 0.1%
2007405 2
< 0.1%
2007404 2
< 0.1%
2007403 2
< 0.1%

data_type
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing114578
Missing (%)76.4%
Memory size2.3 MiB
sub_sample_food
26431 
market_acquisition
5706 
sample_food
 
2208
agricultural_acquisition
 
810
foundation_food
 
223

Length

Max length24
Median length15
Mean length15.440274
Min length11

Characters and Unicode

Total characters546246
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsample_food
2nd rowmarket_acquisition
3rd rowmarket_acquisition
4th rowsub_sample_food
5th rowsub_sample_food

Common Values

ValueCountFrequency (%)
sub_sample_food 26431
 
17.6%
market_acquisition 5706
 
3.8%
sample_food 2208
 
1.5%
agricultural_acquisition 810
 
0.5%
foundation_food 223
 
0.1%
(Missing) 114578
76.4%

Length

2023-06-21T20:06:02.991349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-21T20:06:03.082003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
sub_sample_food 26431
74.7%
market_acquisition 5706
 
16.1%
sample_food 2208
 
6.2%
agricultural_acquisition 810
 
2.3%
foundation_food 223
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 64686
11.8%
_ 61809
11.3%
s 61586
11.3%
a 42704
 
7.8%
u 34790
 
6.4%
m 34345
 
6.3%
e 34345
 
6.3%
l 30259
 
5.5%
d 29085
 
5.3%
f 29085
 
5.3%
Other values (10) 123552
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 484437
88.7%
Connector Punctuation 61809
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 64686
13.4%
s 61586
12.7%
a 42704
8.8%
u 34790
 
7.2%
m 34345
 
7.1%
e 34345
 
7.1%
l 30259
 
6.2%
d 29085
 
6.0%
f 29085
 
6.0%
p 28639
 
5.9%
Other values (9) 94913
19.6%
Connector Punctuation
ValueCountFrequency (%)
_ 61809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 484437
88.7%
Common 61809
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 64686
13.4%
s 61586
12.7%
a 42704
8.8%
u 34790
 
7.2%
m 34345
 
7.1%
e 34345
 
7.1%
l 30259
 
6.2%
d 29085
 
6.0%
f 29085
 
6.0%
p 28639
 
5.9%
Other values (9) 94913
19.6%
Common
ValueCountFrequency (%)
_ 61809
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 546246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 64686
11.8%
_ 61809
11.3%
s 61586
11.3%
a 42704
 
7.8%
u 34790
 
6.4%
m 34345
 
6.3%
e 34345
 
6.3%
l 30259
 
5.5%
d 29085
 
5.3%
f 29085
 
5.3%
Other values (10) 123552
22.6%

description
Text

MISSING 

Distinct11406
Distinct (%)32.2%
Missing114586
Missing (%)76.4%
Memory size2.3 MiB
2023-06-21T20:06:03.332000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length128
Median length110
Mean length39.958128
Min length3

Characters and Unicode

Total characters1413319
Distinct characters78
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10969 ?
Unique (%)31.0%

Sample

1st rowHUMMUS, SABRA CLASSIC
2nd rowHUMMUS, SABRA CLASSIC
3rd rowHUMMUS, SABRA CLASSIC
4th rowHummus
5th rowHummus
ValueCountFrequency (%)
9408
 
4.6%
milk 5613
 
2.7%
shelf 5427
 
2.6%
stable 5427
 
2.6%
plain 4470
 
2.2%
unsweetened 4135
 
2.0%
oil 2940
 
1.4%
mushrooms 2881
 
1.4%
raw 2617
 
1.3%
cheese 2586
 
1.3%
Other values (10838) 160297
77.9%
2023-06-21T20:06:03.706594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172292
 
12.2%
, 86851
 
6.1%
E 70733
 
5.0%
e 53339
 
3.8%
A 50771
 
3.6%
N 50141
 
3.5%
S 44362
 
3.1%
L 43277
 
3.1%
I 41190
 
2.9%
T 39622
 
2.8%
Other values (68) 760741
53.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 647236
45.8%
Lowercase Letter 407791
28.9%
Space Separator 172292
 
12.2%
Other Punctuation 93028
 
6.6%
Decimal Number 56891
 
4.0%
Dash Punctuation 15210
 
1.1%
Close Punctuation 10408
 
0.7%
Open Punctuation 10408
 
0.7%
Connector Punctuation 54
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53339
13.1%
a 37245
 
9.1%
r 33301
 
8.2%
s 31766
 
7.8%
n 30531
 
7.5%
o 30025
 
7.4%
i 29732
 
7.3%
t 24995
 
6.1%
l 17746
 
4.4%
d 17417
 
4.3%
Other values (17) 101694
24.9%
Uppercase Letter
ValueCountFrequency (%)
E 70733
 
10.9%
A 50771
 
7.8%
N 50141
 
7.7%
S 44362
 
6.9%
L 43277
 
6.7%
I 41190
 
6.4%
T 39622
 
6.1%
O 36779
 
5.7%
R 33444
 
5.2%
F 26332
 
4.1%
Other values (16) 210585
32.5%
Decimal Number
ValueCountFrequency (%)
0 16360
28.8%
1 15081
26.5%
2 9114
16.0%
9 4087
 
7.2%
4 3033
 
5.3%
3 2644
 
4.6%
6 2011
 
3.5%
7 1652
 
2.9%
8 1488
 
2.6%
5 1421
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 86851
93.4%
% 2622
 
2.8%
/ 2103
 
2.3%
& 745
 
0.8%
; 374
 
0.4%
. 189
 
0.2%
" 60
 
0.1%
# 43
 
< 0.1%
' 41
 
< 0.1%
Space Separator
ValueCountFrequency (%)
172292
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10408
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10408
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1055027
74.6%
Common 358292
 
25.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 70733
 
6.7%
e 53339
 
5.1%
A 50771
 
4.8%
N 50141
 
4.8%
S 44362
 
4.2%
L 43277
 
4.1%
I 41190
 
3.9%
T 39622
 
3.8%
a 37245
 
3.5%
O 36779
 
3.5%
Other values (43) 587568
55.7%
Common
ValueCountFrequency (%)
172292
48.1%
, 86851
24.2%
0 16360
 
4.6%
- 15210
 
4.2%
1 15081
 
4.2%
) 10408
 
2.9%
( 10408
 
2.9%
2 9114
 
2.5%
9 4087
 
1.1%
4 3033
 
0.8%
Other values (15) 15448
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1413318
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172292
 
12.2%
, 86851
 
6.1%
E 70733
 
5.0%
e 53339
 
3.8%
A 50771
 
3.6%
N 50141
 
3.5%
S 44362
 
3.1%
L 43277
 
3.1%
I 41190
 
2.9%
T 39622
 
2.8%
Other values (67) 760740
53.8%
None
ValueCountFrequency (%)
é 1
100.0%

food_category_id
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)0.1%
Missing114600
Missing (%)76.4%
Infinite0
Infinite (%)0.0%
Mean9.1607365
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:03.795268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q313
95-th percentile19
Maximum25
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6993886
Coefficient of variation (CV)0.62215397
Kurtosis-0.50145529
Mean9.1607365
Median Absolute Deviation (MAD)5
Skewness0.20859651
Sum323887
Variance32.48303
MonotonicityNot monotonic
2023-06-21T20:06:03.864803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11 6856
 
4.6%
1 6406
 
4.3%
9 5910
 
3.9%
16 3603
 
2.4%
4 2924
 
1.9%
14 1860
 
1.2%
5 1503
 
1.0%
20 1282
 
0.9%
15 913
 
0.6%
7 795
 
0.5%
Other values (8) 3304
 
2.2%
(Missing) 114600
76.4%
ValueCountFrequency (%)
1 6406
4.3%
2 386
 
0.3%
4 2924
1.9%
5 1503
 
1.0%
6 568
 
0.4%
7 795
 
0.5%
9 5910
3.9%
10 613
 
0.4%
11 6856
4.6%
12 267
 
0.2%
ValueCountFrequency (%)
25 474
 
0.3%
20 1282
 
0.9%
19 54
 
< 0.1%
18 488
 
0.3%
16 3603
2.4%
15 913
 
0.6%
14 1860
 
1.2%
13 454
 
0.3%
12 267
 
0.2%
11 6856
4.6%

publication_date
Date

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing114578
Missing (%)76.4%
Memory size2.3 MiB
Minimum2019-04-01 00:00:00
Maximum2021-10-28 00:00:00
2023-06-21T20:06:03.937777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:06:04.000817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

id
Real number (ℝ)

MISSING 

Distinct114578
Distinct (%)100.0%
Missing35378
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean6787445.7
Minimum1001
Maximum26845143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:04.084316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2222558.9
Q12245552.2
median2274245.5
Q38529199.8
95-th percentile24468563
Maximum26845143
Range26844142
Interquartile range (IQR)6283647.5

Descriptive statistics

Standard deviation7174508.5
Coefficient of variation (CV)1.0570263
Kurtosis0.89886655
Mean6787445.7
Median Absolute Deviation (MAD)43533
Skewness1.4983761
Sum7.7769195 × 1011
Variance5.1473573 × 1013
MonotonicityNot monotonic
2023-06-21T20:06:04.178083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8519641 1
 
< 0.1%
8519689 1
 
< 0.1%
8519697 1
 
< 0.1%
8519698 1
 
< 0.1%
8519644 1
 
< 0.1%
8519645 1
 
< 0.1%
8519643 1
 
< 0.1%
8519642 1
 
< 0.1%
8519640 1
 
< 0.1%
8519637 1
 
< 0.1%
Other values (114568) 114568
76.4%
(Missing) 35378
 
23.6%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
26845143 1
< 0.1%
24474339 1
< 0.1%
24474338 1
< 0.1%
24474337 1
< 0.1%
24474336 1
< 0.1%
24474335 1
< 0.1%
24474334 1
< 0.1%
24474332 1
< 0.1%
24474331 1
< 0.1%
24474330 1
< 0.1%

nutrient_id
Real number (ℝ)

MISSING 

Distinct221
Distinct (%)0.2%
Missing35851
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean1219.1721
Minimum1002
Maximum2063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:04.267957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1004
Q11091
median1162
Q31289
95-th percentile2006
Maximum2063
Range1061
Interquartile range (IQR)198

Descriptive statistics

Standard deviation228.66961
Coefficient of variation (CV)0.18756139
Kurtosis5.9387737
Mean1219.1721
Median Absolute Deviation (MAD)101
Skewness2.3880506
Sum1.3911363 × 108
Variance52289.79
MonotonicityNot monotonic
2023-06-21T20:06:04.372167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051 3028
 
2.0%
1004 2998
 
2.0%
1098 2737
 
1.8%
1090 2737
 
1.8%
1091 2736
 
1.8%
1101 2736
 
1.8%
1087 2735
 
1.8%
1095 2735
 
1.8%
1089 2731
 
1.8%
1092 2724
 
1.8%
Other values (211) 86208
57.5%
(Missing) 35851
23.9%
ValueCountFrequency (%)
1002 1915
1.3%
1003 1188
 
0.8%
1004 2998
2.0%
1005 177
 
0.1%
1007 2128
1.4%
1008 135
 
0.1%
1009 1042
 
0.7%
1010 887
 
0.6%
1011 890
 
0.6%
1012 889
 
0.6%
ValueCountFrequency (%)
2063 18
 
< 0.1%
2062 34
 
< 0.1%
2061 34
 
< 0.1%
2060 34
 
< 0.1%
2059 91
0.1%
2057 91
0.1%
2053 36
 
< 0.1%
2052 70
< 0.1%
2051 18
 
< 0.1%
2050 18
 
< 0.1%

amount
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5984
Distinct (%)5.2%
Missing35851
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean77.309247
Minimum0
Maximum40700
Zeros25966
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:04.472105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.22
Q36.31
95-th percentile318
Maximum40700
Range40700
Interquartile range (IQR)6.308

Descriptive statistics

Standard deviation671.96277
Coefficient of variation (CV)8.691881
Kurtosis2512.9354
Mean77.309247
Median Absolute Deviation (MAD)0.22
Skewness45.275383
Sum8821371.6
Variance451533.97
MonotonicityNot monotonic
2023-06-21T20:06:04.562745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25966
 
17.3%
0.001 1917
 
1.3%
0.002 1754
 
1.2%
0.003 1456
 
1.0%
0.004 1022
 
0.7%
0.005 912
 
0.6%
0.006 717
 
0.5%
0.007 710
 
0.5%
0.01 689
 
0.5%
0.008 628
 
0.4%
Other values (5974) 78334
52.2%
(Missing) 35851
23.9%
ValueCountFrequency (%)
0 25966
17.3%
3.75 × 10-51
 
< 0.1%
6.25 × 10-51
 
< 0.1%
0.000125 11
 
< 0.1%
0.00025 2
 
< 0.1%
0.0003 2
 
< 0.1%
0.000375 3
 
< 0.1%
0.0004375 2
 
< 0.1%
0.0005 7
 
< 0.1%
0.0005625 4
 
< 0.1%
ValueCountFrequency (%)
40700 1
< 0.1%
40300 1
< 0.1%
40100 1
< 0.1%
39600 1
< 0.1%
39500 1
< 0.1%
39400 1
< 0.1%
39100 1
< 0.1%
39000 2
< 0.1%
38900 1
< 0.1%
38700 2
< 0.1%

data_points
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)< 0.1%
Missing38324
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean2.030493
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:04.660762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum252
Range251
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2649315
Coefficient of variation (CV)3.577915
Kurtosis547.15659
Mean2.030493
Median Absolute Deviation (MAD)0
Skewness19.947138
Sum226668
Variance52.77923
MonotonicityNot monotonic
2023-06-21T20:06:04.752109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100988
67.3%
8 2347
 
1.6%
6 1685
 
1.1%
12 1143
 
0.8%
2 1127
 
0.8%
4 810
 
0.5%
3 597
 
0.4%
5 520
 
0.3%
11 442
 
0.3%
24 204
 
0.1%
Other values (41) 1769
 
1.2%
(Missing) 38324
 
25.6%
ValueCountFrequency (%)
1 100988
67.3%
2 1127
 
0.8%
3 597
 
0.4%
4 810
 
0.5%
5 520
 
0.3%
6 1685
 
1.1%
7 177
 
0.1%
8 2347
 
1.6%
9 154
 
0.1%
10 47
 
< 0.1%
ValueCountFrequency (%)
252 34
< 0.1%
250 2
 
< 0.1%
136 34
< 0.1%
135 2
 
< 0.1%
126 34
< 0.1%
125 2
 
< 0.1%
112 36
< 0.1%
78 34
< 0.1%
77 2
 
< 0.1%
73 65
< 0.1%

derivation_id
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing35860
Missing (%)23.9%
Memory size2.3 MiB
1.0
111623 
4.0
 
1686
49.0
 
787

Length

Max length4
Median length3
Mean length3.0068977
Min length3

Characters and Unicode

Total characters343075
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 111623
74.4%
4.0 1686
 
1.1%
49.0 787
 
0.5%
(Missing) 35860
 
23.9%

Length

2023-06-21T20:06:04.831998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-21T20:06:04.914981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 111623
97.8%
4.0 1686
 
1.5%
49.0 787
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 114096
33.3%
0 114096
33.3%
1 111623
32.5%
4 2473
 
0.7%
9 787
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 228979
66.7%
Other Punctuation 114096
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 114096
49.8%
1 111623
48.7%
4 2473
 
1.1%
9 787
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 114096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 343075
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 114096
33.3%
0 114096
33.3%
1 111623
32.5%
4 2473
 
0.7%
9 787
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 343075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 114096
33.3%
0 114096
33.3%
1 111623
32.5%
4 2473
 
0.7%
9 787
 
0.2%

min
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1776
Distinct (%)16.4%
Missing139150
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean41.144909
Minimum0
Maximum37200
Zeros3430
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:05.002252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.06
Q31.91
95-th percentile122
Maximum37200
Range37200
Interquartile range (IQR)1.91

Descriptive statistics

Standard deviation552.77724
Coefficient of variation (CV)13.434888
Kurtosis3790.744
Mean41.144909
Median Absolute Deviation (MAD)0.06
Skewness57.38916
Sum444611.88
Variance305562.68
MonotonicityNot monotonic
2023-06-21T20:06:05.097080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3430
 
2.3%
0.002 157
 
0.1%
0.001 136
 
0.1%
0.003 102
 
0.1%
0.007 79
 
0.1%
0.01 77
 
0.1%
0.004 66
 
< 0.1%
0.03 65
 
< 0.1%
0.008 63
 
< 0.1%
0.006 62
 
< 0.1%
Other values (1766) 6569
 
4.4%
(Missing) 139150
92.8%
ValueCountFrequency (%)
0 3430
2.3%
0.001 136
 
0.1%
0.0015 2
 
< 0.1%
0.002 157
 
0.1%
0.0025 1
 
< 0.1%
0.003 102
 
0.1%
0.0035 2
 
< 0.1%
0.004 66
 
< 0.1%
0.005 61
 
< 0.1%
0.006 62
 
< 0.1%
ValueCountFrequency (%)
37200 2
< 0.1%
8930 1
< 0.1%
5320 2
< 0.1%
5150 2
< 0.1%
5020 2
< 0.1%
4460 1
< 0.1%
4070 1
< 0.1%
3860 1
< 0.1%
3410 2
< 0.1%
3030 2
< 0.1%

max
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2019
Distinct (%)18.7%
Missing139150
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean64.185261
Minimum0
Maximum40700
Zeros2422
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:05.304877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.176
Q33.7
95-th percentile208.75
Maximum40700
Range40700
Interquartile range (IQR)3.698

Descriptive statistics

Standard deviation671.24011
Coefficient of variation (CV)10.457854
Kurtosis2530.3829
Mean64.185261
Median Absolute Deviation (MAD)0.176
Skewness44.337477
Sum693585.93
Variance450563.28
MonotonicityNot monotonic
2023-06-21T20:06:05.394402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2422
 
1.6%
0.002 165
 
0.1%
0.003 148
 
0.1%
0.001 141
 
0.1%
0.006 99
 
0.1%
0.004 97
 
0.1%
0.01 84
 
0.1%
0.005 80
 
0.1%
0.007 73
 
< 0.1%
0.008 60
 
< 0.1%
Other values (2009) 7437
 
5.0%
(Missing) 139150
92.8%
ValueCountFrequency (%)
0 2422
1.6%
0.0003 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.001 141
 
0.1%
0.002 165
 
0.1%
0.003 148
 
0.1%
0.004 97
 
0.1%
0.005 80
 
0.1%
0.0055 1
 
< 0.1%
0.006 99
 
0.1%
ValueCountFrequency (%)
40700 2
< 0.1%
14600 1
< 0.1%
9630 2
< 0.1%
8750 2
< 0.1%
8560 1
< 0.1%
7430 2
< 0.1%
7110 2
< 0.1%
7060 2
< 0.1%
6600 1
< 0.1%
5040 2
< 0.1%

median
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2146
Distinct (%)18.8%
Missing138548
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean49.110512
Minimum0
Maximum38500
Zeros2967
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:05.491994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.13
Q33
95-th percentile158
Maximum38500
Range38500
Interquartile range (IQR)3

Descriptive statistics

Standard deviation585.24524
Coefficient of variation (CV)11.916904
Kurtosis3299.6661
Mean49.110512
Median Absolute Deviation (MAD)0.13
Skewness52.096074
Sum560252.72
Variance342512
MonotonicityNot monotonic
2023-06-21T20:06:05.580625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2967
 
2.0%
0.002 182
 
0.1%
0.001 135
 
0.1%
0.003 125
 
0.1%
0.004 97
 
0.1%
0.005 91
 
0.1%
0.01 87
 
0.1%
0.02 68
 
< 0.1%
0.07 62
 
< 0.1%
0.006 61
 
< 0.1%
Other values (2136) 7533
 
5.0%
(Missing) 138548
92.4%
ValueCountFrequency (%)
0 2967
2.0%
0.00025 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.00075 2
 
< 0.1%
0.001 135
 
0.1%
0.001373 1
 
< 0.1%
0.0015 1
 
< 0.1%
0.00185 1
 
< 0.1%
0.002 182
 
0.1%
0.00225 2
 
< 0.1%
ValueCountFrequency (%)
38500 2
< 0.1%
12800 1
< 0.1%
7860 2
< 0.1%
6550 2
< 0.1%
5800 2
< 0.1%
5760 1
< 0.1%
5560 2
< 0.1%
5040 2
< 0.1%
4280 1
< 0.1%
4210 1
< 0.1%

footnote
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing149953
Missing (%)> 99.9%
Memory size2.3 MiB
Trace amount

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters36
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrace amount
2nd rowTrace amount
3rd rowTrace amount

Common Values

ValueCountFrequency (%)
Trace amount 3
 
< 0.1%
(Missing) 149953
> 99.9%

Length

2023-06-21T20:06:05.665248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-21T20:06:05.733228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
trace 3
50.0%
amount 3
50.0%

Most occurring characters

ValueCountFrequency (%)
a 6
16.7%
T 3
8.3%
r 3
8.3%
c 3
8.3%
e 3
8.3%
3
8.3%
m 3
8.3%
o 3
8.3%
u 3
8.3%
n 3
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30
83.3%
Uppercase Letter 3
 
8.3%
Space Separator 3
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6
20.0%
r 3
10.0%
c 3
10.0%
e 3
10.0%
m 3
10.0%
o 3
10.0%
u 3
10.0%
n 3
10.0%
t 3
10.0%
Uppercase Letter
ValueCountFrequency (%)
T 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33
91.7%
Common 3
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6
18.2%
T 3
9.1%
r 3
9.1%
c 3
9.1%
e 3
9.1%
m 3
9.1%
o 3
9.1%
u 3
9.1%
n 3
9.1%
t 3
9.1%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6
16.7%
T 3
8.3%
r 3
8.3%
c 3
8.3%
e 3
8.3%
3
8.3%
m 3
8.3%
o 3
8.3%
u 3
8.3%
n 3
8.3%

min_year_acqured
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)0.1%
Missing124305
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean2014.9956
Minimum1999
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:05.789822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2001
Q12016
median2016
Q32016
95-th percentile2020
Maximum2021
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1825708
Coefficient of variation (CV)0.0020757221
Kurtosis5.29866
Mean2014.9956
Median Absolute Deviation (MAD)0
Skewness-2.2196443
Sum51686652
Variance17.493899
MonotonicityNot monotonic
2023-06-21T20:06:05.853493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2016 16288
 
10.9%
2020 1469
 
1.0%
2018 1179
 
0.8%
2009 870
 
0.6%
2011 838
 
0.6%
2013 739
 
0.5%
2001 718
 
0.5%
2015 625
 
0.4%
2019 597
 
0.4%
2021 538
 
0.4%
Other values (9) 1790
 
1.2%
(Missing) 124305
82.9%
ValueCountFrequency (%)
1999 162
 
0.1%
2000 414
0.3%
2001 718
0.5%
2003 99
 
0.1%
2006 38
 
< 0.1%
2008 163
 
0.1%
2009 870
0.6%
2010 137
 
0.1%
2011 838
0.6%
2012 348
 
0.2%
ValueCountFrequency (%)
2021 538
 
0.4%
2020 1469
 
1.0%
2019 597
 
0.4%
2018 1179
 
0.8%
2017 278
 
0.2%
2016 16288
10.9%
2015 625
 
0.4%
2014 151
 
0.1%
2013 739
 
0.5%
2012 348
 
0.2%

name
Text

MISSING 

Distinct472
Distinct (%)99.8%
Missing149483
Missing (%)99.7%
Memory size2.3 MiB
2023-06-21T20:06:06.000316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length68
Median length40
Mean length14.881607
Min length2

Characters and Unicode

Total characters7039
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique471 ?
Unique (%)99.6%

Sample

1st rowEnergy (Atwater General Factors)
2nd rowEnergy (Atwater Specific Factors)
3rd rowSolids
4th rowNitrogen
5th rowProtein
ValueCountFrequency (%)
acid 40
 
3.9%
pufa 35
 
3.5%
total 33
 
3.3%
vitamin 29
 
2.9%
c 21
 
2.1%
sfa 21
 
2.1%
mufa 19
 
1.9%
acids 17
 
1.7%
fatty 14
 
1.4%
fiber 11
 
1.1%
Other values (515) 774
76.3%
2023-06-21T20:06:06.262128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 556
 
7.9%
544
 
7.7%
a 472
 
6.7%
e 418
 
5.9%
n 410
 
5.8%
t 410
 
5.8%
o 398
 
5.7%
l 303
 
4.3%
c 282
 
4.0%
r 277
 
3.9%
Other values (62) 2969
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4722
67.1%
Uppercase Letter 948
 
13.5%
Space Separator 544
 
7.7%
Decimal Number 382
 
5.4%
Other Punctuation 256
 
3.6%
Dash Punctuation 93
 
1.3%
Open Punctuation 41
 
0.6%
Close Punctuation 41
 
0.6%
Math Symbol 12
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 556
11.8%
a 472
10.0%
e 418
8.9%
n 410
8.7%
t 410
8.7%
o 398
8.4%
l 303
 
6.4%
c 282
 
6.0%
r 277
 
5.9%
s 213
 
4.5%
Other values (16) 983
20.8%
Uppercase Letter
ValueCountFrequency (%)
F 141
14.9%
A 141
14.9%
P 77
 
8.1%
S 68
 
7.2%
C 66
 
7.0%
U 61
 
6.4%
T 60
 
6.3%
M 39
 
4.1%
V 35
 
3.7%
E 34
 
3.6%
Other values (14) 226
23.8%
Decimal Number
ValueCountFrequency (%)
1 99
25.9%
2 81
21.2%
0 42
11.0%
3 35
 
9.2%
6 31
 
8.1%
8 28
 
7.3%
4 24
 
6.3%
5 21
 
5.5%
7 16
 
4.2%
9 5
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 159
62.1%
: 87
34.0%
. 6
 
2.3%
' 2
 
0.8%
; 1
 
0.4%
/ 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 11
91.7%
> 1
 
8.3%
Space Separator
ValueCountFrequency (%)
544
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5670
80.6%
Common 1369
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 556
 
9.8%
a 472
 
8.3%
e 418
 
7.4%
n 410
 
7.2%
t 410
 
7.2%
o 398
 
7.0%
l 303
 
5.3%
c 282
 
5.0%
r 277
 
4.9%
s 213
 
3.8%
Other values (40) 1931
34.1%
Common
ValueCountFrequency (%)
544
39.7%
, 159
 
11.6%
1 99
 
7.2%
- 93
 
6.8%
: 87
 
6.4%
2 81
 
5.9%
0 42
 
3.1%
( 41
 
3.0%
) 41
 
3.0%
3 35
 
2.6%
Other values (12) 147
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 556
 
7.9%
544
 
7.7%
a 472
 
6.7%
e 418
 
5.9%
n 410
 
5.8%
t 410
 
5.8%
o 398
 
5.7%
l 303
 
4.3%
c 282
 
4.0%
r 277
 
3.9%
Other values (62) 2969
42.2%

unit_name
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)2.5%
Missing149483
Missing (%)99.7%
Memory size2.3 MiB
G
204 
MG
184 
UG
69 
KCAL
 
3
IU
 
3
Other values (7)
 
10

Length

Max length7
Median length2
Mean length1.653277
Min length1

Characters and Unicode

Total characters782
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.1%

Sample

1st rowKCAL
2nd rowKCAL
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 204
 
0.1%
MG 184
 
0.1%
UG 69
 
< 0.1%
KCAL 3
 
< 0.1%
IU 3
 
< 0.1%
UMOL_TE 3
 
< 0.1%
MCG_RE 2
 
< 0.1%
PH 1
 
< 0.1%
SP_GR 1
 
< 0.1%
kJ 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 149483
99.7%

Length

2023-06-21T20:06:06.353542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 204
43.1%
mg 184
38.9%
ug 69
 
14.6%
kcal 3
 
0.6%
iu 3
 
0.6%
umol_te 3
 
0.6%
mcg_re 2
 
0.4%
ph 1
 
0.2%
sp_gr 1
 
0.2%
kj 1
 
0.2%
Other values (2) 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
G 463
59.2%
M 191
24.4%
U 75
 
9.6%
_ 8
 
1.0%
E 7
 
0.9%
L 6
 
0.8%
C 5
 
0.6%
A 5
 
0.6%
T 4
 
0.5%
I 3
 
0.4%
Other values (8) 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 773
98.8%
Connector Punctuation 8
 
1.0%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 463
59.9%
M 191
24.7%
U 75
 
9.7%
E 7
 
0.9%
L 6
 
0.8%
C 5
 
0.6%
A 5
 
0.6%
T 4
 
0.5%
I 3
 
0.4%
O 3
 
0.4%
Other values (6) 11
 
1.4%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 774
99.0%
Common 8
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 463
59.8%
M 191
24.7%
U 75
 
9.7%
E 7
 
0.9%
L 6
 
0.8%
C 5
 
0.6%
A 5
 
0.6%
T 4
 
0.5%
I 3
 
0.4%
O 3
 
0.4%
Other values (7) 12
 
1.6%
Common
ValueCountFrequency (%)
_ 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 463
59.2%
M 191
24.4%
U 75
 
9.6%
_ 8
 
1.0%
E 7
 
0.9%
L 6
 
0.8%
C 5
 
0.6%
A 5
 
0.6%
T 4
 
0.5%
I 3
 
0.4%
Other values (8) 15
 
1.9%

nutrient_nbr
Real number (ℝ)

MISSING 

Distinct461
Distinct (%)100.0%
Missing149495
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean516.19217
Minimum200
Maximum958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:06.436555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile225
Q1321.2
median510
Q3691
95-th percentile837
Maximum958
Range758
Interquartile range (IQR)369.8

Descriptive statistics

Standard deviation208.32844
Coefficient of variation (CV)0.40358698
Kurtosis-1.248768
Mean516.19217
Median Absolute Deviation (MAD)186
Skewness0.15583541
Sum237964.59
Variance43400.737
MonotonicityNot monotonic
2023-06-21T20:06:06.522105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
675 1
 
< 0.1%
673 1
 
< 0.1%
672 1
 
< 0.1%
671 1
 
< 0.1%
670 1
 
< 0.1%
669 1
 
< 0.1%
668 1
 
< 0.1%
667 1
 
< 0.1%
666 1
 
< 0.1%
665 1
 
< 0.1%
Other values (451) 451
 
0.3%
(Missing) 149495
99.7%
ValueCountFrequency (%)
200 1
< 0.1%
201 1
< 0.1%
202 1
< 0.1%
203 1
< 0.1%
204 1
< 0.1%
205 1
< 0.1%
205.2 1
< 0.1%
206 1
< 0.1%
207 1
< 0.1%
208 1
< 0.1%
ValueCountFrequency (%)
958 1
< 0.1%
957 1
< 0.1%
956 1
< 0.1%
955 1
< 0.1%
954 1
< 0.1%
953 1
< 0.1%
952 1
< 0.1%
951 1
< 0.1%
950 1
< 0.1%
861 1
< 0.1%

rank
Real number (ℝ)

MISSING 

Distinct352
Distinct (%)76.2%
Missing149494
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean235701.64
Minimum50
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2023-06-21T20:06:06.620389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1326.05
Q17252.5
median14550
Q322075
95-th percentile999999
Maximum999999
Range999949
Interquartile range (IQR)14822.5

Descriptive statistics

Standard deviation414985
Coefficient of variation (CV)1.7606368
Kurtosis-0.29677632
Mean235701.64
Median Absolute Deviation (MAD)7325
Skewness1.3051822
Sum1.0889416 × 108
Variance1.7221255 × 1011
MonotonicityNot monotonic
2023-06-21T20:06:06.711138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 105
 
0.1%
16211 2
 
< 0.1%
15100 2
 
< 0.1%
7500 2
 
< 0.1%
8730 2
 
< 0.1%
6250 2
 
< 0.1%
14250 2
 
< 0.1%
15601 1
 
< 0.1%
19200 1
 
< 0.1%
19100 1
 
< 0.1%
Other values (342) 342
 
0.2%
(Missing) 149494
99.7%
ValueCountFrequency (%)
50 1
< 0.1%
100 1
< 0.1%
200 1
< 0.1%
280 1
< 0.1%
290 1
< 0.1%
300 1
< 0.1%
400 1
< 0.1%
500 1
< 0.1%
600 1
< 0.1%
700 1
< 0.1%
ValueCountFrequency (%)
999999 105
0.1%
23100 1
 
< 0.1%
23000 1
 
< 0.1%
22900 1
 
< 0.1%
22800 1
 
< 0.1%
22700 1
 
< 0.1%
22600 1
 
< 0.1%
22500 1
 
< 0.1%
22400 1
 
< 0.1%
22300 1
 
< 0.1%

Interactions

2023-06-21T20:06:00.281392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:49.957566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-06-21T20:05:54.751309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:55.664108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:56.672998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-06-21T20:05:52.470064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-06-21T20:06:00.965023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:50.930622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:51.687958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:52.616905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:53.651472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:54.686917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:55.606347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:56.609457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:57.552756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:58.498955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:05:59.461385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-21T20:06:00.206593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-06-21T20:06:01.145599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-21T20:06:01.518890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-21T20:06:02.264068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fdc_iddata_typedescriptionfood_category_idpublication_dateidnutrient_idamountdata_pointsderivation_idminmaxmedianfootnotemin_year_acqurednameunit_namenutrient_nbrrank
0319874.0sample_foodHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1319875.0market_acquisitionHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2319876.0market_acquisitionHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3319877.0sub_sample_foodHummus16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4319878.0sub_sample_foodHummus16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5319879.0sample_foodHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6319880.0market_acquisitionHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7319881.0market_acquisitionHUMMUS, SABRA CLASSIC16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8319882.0sub_sample_foodHummus16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9319883.0sub_sample_foodHummus16.02019-04-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
fdc_iddata_typedescriptionfood_category_idpublication_dateidnutrient_idamountdata_pointsderivation_idminmaxmedianfootnotemin_year_acqurednameunit_namenutrient_nbrrank
463NaNNaNNaNNaNNaN2050.0NaNNaNNaNNaNNaNNaNNaNNaNNaNGenistinMG718.019320.0
464NaNNaNNaNNaNNaN2051.0NaNNaNNaNNaNNaNNaNNaNNaNNaNGlycitinMG719.019330.0
465NaNNaNNaNNaNNaN2057.0NaNNaNNaNNaNNaNNaNNaNNaNNaNErgothionineMGNaN16255.0
466NaNNaNNaNNaNNaN2058.0NaNNaNNaNNaNNaNNaNNaNNaNNaNBeta-glucanGNaN1327.0
467NaNNaNNaNNaNNaN2059.0NaNNaNNaNNaNNaNNaNNaNNaNNaNVitamin D4UGNaN8730.0
468NaNNaNNaNNaNNaN2060.0NaNNaNNaNNaNNaNNaNNaNNaNNaNErgosta-7-enolMGNaN16210.0
469NaNNaNNaNNaNNaN2061.0NaNNaNNaNNaNNaNNaNNaNNaNNaNErgosta-7,22-dienolMGNaN16211.0
470NaNNaNNaNNaNNaN2062.0NaNNaNNaNNaNNaNNaNNaNNaNNaNErgosta-5,7-dienolMGNaN16211.0
471NaNNaNNaNNaNNaN2063.0NaNNaNNaNNaNNaNNaNNaNNaNNaNVerbascoseGNaN2450.0
472NaNNaNNaNNaNNaN2064.0NaNNaNNaNNaNNaNNaNNaNNaNNaNOligiosaccharidesMGNaN2250.0